Skin cancer is a significant health problem in the United States. It has been reported that one of five Americans will get some form of skin cancer in their lifetime. Currently, nearly half of new cancers are diagnosed as skin cancers. Malignant melanoma, the most fatal skin cancer, first forms at the upper layers of skin. When metastasized, cancerous cells from melanoma enter blood vessels and proliferate throughout the body. Malignant melanoma is highly fatal if not detected in early stages. However, it can be cured with nearly 100% survival rate if removed at an early stage.
Physicians usually use the “ABCD” rule to determine if a lesion under investigation is malignant melanoma. The acronym “ABCD” refers to asymmetry, border, color and diameter, respectively. Malignant melanoma typically has an asymmetrical shape, an uneven border, varied colors and a large diameter. Once a suspicious lesion is excised a diagnosis can be confirmed by other instruments. However, neither visual inspection using the “ABCD” rule nor examination of the excised lesion can provide depth information of the skin cancer, which is a crucial signature to grade the degree of invasion of a skin lesion. Angiogenesis, or increased blood flow, plays a very important role in detection of melanomas in early curable stage. Specific patterns of distribution of melanin, oxy-hemoglobin and de-oxy-hemoglobin can lead to characterization of dysplastic nevi and their potential for transformation into malignant melanoma in very early phases.
Various light transportation models have been used by researchers to reconstruct information to characterize skin-lesions. For example, a Kubelka-Munk model was used to simulate the formation of images of melanoma and presented a method to recover blood and melanin distribution in various skin layers. Claridge et al., An inverse method for recovery of tissue parameters from colour images, Information Processing in Medical Imaging. Springer, Berlin, LNCS2732, pp. 306-317. However, the Kubelka-Munk model is theoretically established in a one-dimensional system with point-based measurements. For more complex geometries, Monte Carlo simulation or Diffusion Approximation has been used in optical tomographic modalities for more accurate reconstructions. The commonly adopted strategy for reconstruction involves dividing the field of view into a number of voxels and assuming constant optical properties in each voxel. The optical properties are then estimated voxel-by-voxel by matching model predicted measurements to the actual measurements. This is a typically under-determined and ill-posed inverse problem as the number of measurements is usually much less than the number of voxels to be reconstructed. In general, the forward process is a mapping from high dimensional space (unknown optical properties of voxels) to low dimensional space (limited measurements). Due to the loss of information during the forward process, the solution to the inverse problem is not unique and usually has to be stabilized through various regularization methods. It is therefore difficult to obtain a quantitatively accurate and well-localized solution. In addition, light photons are quickly diffused in a turbid medium such as human skin. As a result, there is a strong dependence or similarity between different measurements such that increasing the number of measurements would not lead to a dramatic change in the characteristic behavior of the inverse problem.
In recent years, optical medical modalities have drawn significant attention from researchers. Visible and near-infrared light wavelengths have been used in surface reflectance, transillumination and transmission based methods. See, Ganster et al., Computer aided recognition of pigmented skin lesions, Melanoma Research, vol. 7 (1997); Seidenari et al., Digital video-microscopy and image analysis with automatic classification for detection of thin melanomas, Melanoma Research 9(2), 163-171 (1999); Menzies et al., Automated instrumentation and diagnosis of invasive melanoma, Melanoma Research vol. 7, 13 (1997); Claridge et al., From color to tissue histology: Physics-based interpretation of images of pigmented skin lesion, Medical Image Analysis, pp. 489-502 (2003); Tomatis et al., Automated melanoma detection: multi-spectral imaging and neural network approach for classification, Med. Phys. 30(2), pp. 212-221 (2003); Tomatis et al., Spectro-photo-metric imaging of subcutaneous pigmented lesion: Discriminant analysis, optical properties and histological characteristics, J. Photochem. Photobiol., B 42, 32-39 (1998). U.S. Pat. No. 5,146,923 discloses a portable nevoscope which provides a noninvasive means to examine a skin lesion in situ, and provides a means to process and analyze skin lesion data relating to properties such as thickness, color, size, pigmentation, boundary, and texture. Due to the limited view and limited-angle measurements available via the prior art nevoscope, the intrinsic ill-posed and under-determined nature of optical imaging pose problems in reconstructing accurate tomographic information.
Consequently there is a need for an improved nevoscope device and methods of obtaining improved reconstruction results.